A method of determining one or more interference parameters for a particular peak of an isotopic distribution corresponding to a precursor molecule in MS scan data is provided. The MS scan data comprises a plurality of peaks. Each peak has a mass-to-charge ratio and a relative abundance. The isotopic distribution comprises a subset of the plurality of peaks. The one or more interference parameters comprises a peak purity, p.sub.i, for the particular peak. The method comprises determining that there are no interfering peaks relevant to the isotopic distribution and determining that the peak purity, p.sub.i, for the particular peak should be a maximum purity value. Alternatively, the method comprises identifying one or more interfering peaks from the MS scan data, wherein the one or more interfering peaks do not belong to the subset of peaks of the isotopic distribution, and determining the peak purity, p.sub.i, for the particular peak based on: the relative abundance, I.sub.i, of the particular peak, and the relative abundance of the one or more interfering peaks.

The present invention provides rapid, sensitive high-throughput methods and systems for characterizing peptides or proteins using hydrophobic interaction chromatography-coupled native mass spectrometry to improve manufacturing process of biopharmaceutical products, such as identifying impurities during antibody purification, monitoring post-translational modification variants during production, or characterizing drug-to-antibody ratio of antibody-drug conjugates. The separation profiles of the peptides or proteins are generated and compared to identify or qualify the peptides or proteins.

First, an MS scan of a mass range of a control sample that does not include a metabolite is performed (601) producing background peak m/z and intensity values for background precursor ions (602). Background peaks are selected for an exclusion list and in the exclusion list an m/z value and an intensity value are included for each background peak (604). Next, an MS scan of the mass range of an experimental sample that does include a metabolite is performed (610) producing peak m/z and intensity values for precursor ions (612). Peaks are selected for a peak list and in the peak list an m/z value and an intensity value are included for each peak (614). Finally, each peak of the peak list that has both an m/z value and an intensity value that correspond to an m/z value and an intensity value of a background peak of the exclusion list is excluded from the peak list (616).

Disclosed is a method of associating molecular structures with signal peaks in spectrometry data obtained from separation according to one or more physical-chemical properties, comprising, as the case may be repeatedly: providing one or more signal peaks in acquired spectrometry data being related to an experimental value of mobility or a related property; ascertaining one or more molecular structure candidates suitable for being associated with the one or more signal peaks; providing by one of calculating, estimating, deriving and deducing for each molecular structure candidate a distribution of first match scores as a function of mobility; defining a presumed first match score for each molecular structure candidate as output from the respective distribution on applying the experimental value of mobility of the one or more signal peaks; and using the presumed first match score in a step of associating a molecular structure with the one or more signal peaks.

A method for rapid detection of dry eye syndrome includes collecting a first tear fluid from healthy participant and a second tear fluid from patient with eye dryness; isolating EV samples from the first tear fluid; acquiring a first fingerprint diagram of proteomes of the EV samples from the first tear fluid, the first fingerprint diagram comprises a plurality of first discriminant peaks; isolating EV samples from the second tear fluid; acquiring a second fingerprint diagram of proteomes of the EV samples from the second tear fluid, the second fingerprint diagram comprises a plurality of second discriminant peaks; and comparing the first discriminant peaks and the second discriminant peaks to determine whether the patient has the DES. This is a fast and precise method for detecting the DES of the participant.

To improve the reliability of mutual diagnosis in a cancer determination by machine learning, m/z values of ions originating from tumor markers or similar substances used in other related tests are stored in a particular m/z-value database. A spectrum information filtering section deletes signal intensities at the m/z values stored in the particular m/z-value database from a large number of mass spectra classified by the presence or absence of cancer. Using the data which remain after the deletion as training data, a training processor obtains training-result information and stores it in a training result database. A judgment processor similarly deletes signal intensities at the predetermined m/z values from mass spectrum data obtained for a target sample to be judged. Then, based on the training-result information stored in the training-result database, the judgment processor determines whether the target sample should be classified into a cancerous group or non-cancerous group.

Techniques and apparatus for executing jobs for performing analytical methods are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to receive a job request from a data system to perform a job, and determine an acquisition system to perform the job, the acquisition system to determine at least one task for the job, provide the at least one task to a task sequencer to coordinate performance of the at least one task, and provide data artifacts to the data system resulting from performance of the at least one task. Other embodiments are described.

A method of mass spectrometry is disclosed comprising: a step (10) of analysing a reference compound in a first mass spectrometer and outputting mass spectral data in response thereto; a step (20) of analysing the reference compound in a second, different mass spectrometer and outputting mass spectral data in response thereto; and a step (30) of automatically adjusting an operational parameter, duty cycle (e.g. duty cycle of data acquisition), or acquired spectral data of at least one mass spectrometer such that, for the same (given) consumption of reference compound by the spectrometer, the statistical precision of quantification (the number of detected ions) and/or of mass measurement (the mass resolution) by the mass spectrometer is substantially the same as that of the other mass spectrometer. A similar method of ion mobility spectrometry is disclosed.

A numerical processing method for a nucleic acid mass spectrum, including: step S1, recalibrating a single mass spectrum, for each detection point of a sample, obtaining a plurality of mass spectra corresponding to different positions of the detection point, each mass spectrum being recalibrated by using anchor peaks with an expected mass-to-charge ratio; step S2, synthesizing the mass spectra, where the mass spectra corresponding to the different positions of the detection point are synthesized into a unitary mass spectrum of the detection point; step S3: performing wavelet filtering on the unitary mass spectrum to eliminate high-frequency noise and a baseline through a wavelet-based digital filter; and step S4: extracting a peak feature value, performing peak fitting to obtain a fitted curve of the unitary mass spectrum, and obtaining a peak height, a peak width, a peak area, a mass offset, and a signal-noise ratio based on the fitted curve.

An analysis method for analyzing a sample includes a first step of acquiring measurement data including a first signal based on the sample and a second signal based on noise added to the first signal as a result of analysis of the sample, a second step of assuming a shape representing the first signal and a shape representing the second signal and modeling the measurement data using Bayesian inference, and a third step of estimating a probability distribution of characteristics of the sample based on the modeled measurement data.